{"id":"W4413169675","doi":"10.1093/jbmrpl/ziaf128","title":"Transcriptomic and lipidomic profiling provide novel insight into the pathogenesis of monogenic <i>SGMS2</i> -related osteoporosis","year":2025,"lang":"en","type":"article","venue":"JBMR Plus","topic":"Caveolin-1 and cellular processes","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine","funders":"Biocenter Finland; Samfundet Folkhälsan; Novo Nordisk; Magnus Ehrnroothin Säätiö; Novo Nordisk Fonden; Academy of Finland; Lastentautien Tutkimussäätiö; Folkhälsanin Tutkimussäätiö; Sigrid Juséliuksen Säätiö; China Scholarship Council; Suomen Lääketieteen Säätiö; Korvatautien Tutkimussäätiö; Helsingin Yliopisto","keywords":"Profiling (computer programming); Transcriptome; Osteoporosis; Computational biology; Pathogenesis; Bioinformatics; Medicine; Biology; Genetics; Computer science; Gene; Pathology; Gene expression","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001787282,0.0001766227,0.0002148129,0.00005430705,0.0001156351,0.00002384092,0.0002121237,0.0001848131,0.000004639696],"category_scores_gemma":[0.00005364513,0.0001352253,0.0001214767,0.0001949894,0.0001414495,0.00000557547,0.0001055098,0.00009859919,0.000001796807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001891909,"about_ca_system_score_gemma":0.0002561424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008998645,"about_ca_topic_score_gemma":0.0001191319,"domain_scores_codex":[0.9990001,0.0000347859,0.0003315125,0.0003542706,0.00009340187,0.0001859598],"domain_scores_gemma":[0.9994208,0.00002260434,0.0001012281,0.0003277432,0.00008537147,0.00004224652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001361656,0.00004631454,0.000764726,0.0001541645,0.0001014665,7.039686e-7,0.0006661792,0.00005544299,0.9943184,0.0001544153,0.0000660954,0.003535909],"study_design_scores_gemma":[0.00136366,0.00008700884,0.0006351278,0.00003955929,0.0001333213,0.000009629392,0.0003632497,0.0002980698,0.9893512,0.0001566626,0.007395518,0.0001670169],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824298,0.01492151,0.0008696854,0.0002686258,0.0001899447,0.0003784615,0.00001763052,0.00001534579,0.0009089691],"genre_scores_gemma":[0.9970597,0.001033511,0.0003682988,0.0002704565,0.00005905485,0.00005388163,0.0000572323,0.0000203596,0.001077525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01462985,"threshold_uncertainty_score":0.5514326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00517528028090147,"score_gpt":0.2162836530472244,"score_spread":0.2111083727663229,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}